Python Tricks: The Power Of Decorators
At their core, Python’s decorators allow you to extend and modify the behavior of a callable(functions , methods, and classes) without
Any sufficiently generic functionality you can tack on to an existing class or function’s behavior makes a great use case for decoration. This includes the following:
- logging
- enforcing access control and authentication
- instrumentation and timing functions
- rate-limiting
- caching, and more
Now, why should you master the use of decorators in Python? After all, what I just mentioned sounded quite abstract, and it might be difficult to see how decorators can benefit you in your day-to-day work as a Python developer. Let me try to bring some clarity
to this question by giving you a somewhat real-world example:
Imagine you’ve got 30 functions with business logic in your report-generating program. One rainy Monday morning your boss walks up to your desk and says:“Happy Monday! Remember those TPS reports? I need you to add input/output logging to each step in the report generator. XYZ Corp needs it for auditing purpose. Oh, and I told them we can ship this by Wednesday.”
Depending on whether or not you’ve got a solid grasp on Python’s decorators, this request will either send your blood pressure spiking or leave you relatively calm.😄
Without decorators you might be spending the next three days scrambling to modify each of those 30 functions and clutter them up with manual logging calls. Fun times, right?
If you do know your decorators however, you’ll calmly smile at your boss and say: “Don’t worry Jim, I’ll get it done by 2pm today”
Right after that you’ll type the code for a generic @audit_log decorator(that’s only about 10 lines long) and quickly paste it in front of each function definition. Then you’ll commit your code and grab another cup of coffee…☕️
I’m dramatizing here, but only a little. Decorators can be that powerful. I’d go as far as to say that understanding decorators is a milestone for any serious Python programmer.They require a solid grasp of several advanced concepts in the language, including the properties of first-class functions.
I believe that the payoff for understanding how decorators work in Python can be enormous
Sure, decorators are relatively complicated to wrap your head around for the first time, but they’re a highly useful feature that you’ll often encounter in third-party frameworks and the Python standard library. Explaining decorators is also a make or break
Before you dive in however, now would be an excellent moment to refresh your memory on the properties of first-class functions
Funcitons are objects - they can be assigned to variables and passed to and returned from other functions
Functions can be defined inside other functions - and a child function can capture the parent function’s local state(lexical closures)
Alright, are you ready to do this? Let’s get started.
Python Decorators Basics
Now, what are decorators really? They “decorate” or “wrap” （包装）another function and let you execute code before and after the wrapped function runs.
Decorators allow you to define reusable building blocks that can change or extend the behavior of other functions. And, they let you do that without permanently modifying the wrapped function itself. The function’s behavior changes only when it’s decorated.
What might the implementation of a simple decorator look like? In basic terms, a decorator is a callable that takes a callable as input and returns another callable.
The following function has that property and could be considered the simplest decorators you could possibly write:
def null_decorator(func):
return func
1.
2.
As you can see, null_decortator is a callable (it’s a function), it takes another callable as its input, and it returns the same input callable without modifying it.
Let’s use it to decorate (or wrap)
def greet():
... return 'Hello'
greet = null_decorator(greet)
greet
<function greet at 0x7fb6650e7e50> # greet is a function
greet() # call it
'Hello'
In this example, I’ve defined a greet function and then immediately decorated it by running it through the null_decorator function. I know this doesn’t look very useful yet. I mean, we specially designed the null decorator to be useless, right? But in a moment this example will clarify how Python’s special-case decorator syntax works.
Instead of explicitly calling null_decorator on greet and then reassigning the greet variable, you can use Python’s @ syntax for decorating a function more conveniently:
@null_decorator
def greet():
return 'Hello!'
>>> greet()
'Hello!'
Putting an @null_decorator line in front of the function definition is the same as defining the function first and then running through the decorator. Using the @ syntax is just syntactic sugar
Note that using the @syntax decorates the function immediately at definition time. This makes it difficult to access the undecorated original without brittle hacks. Therefore you might choose to decorate some functions manually in order to retain the ability to call the undecorated function as well.
Decorators Can Modify Behavior
Now that you’re a little more familiar with the decorator syntax, let’s write another decorator that actually does something
Here’s a slightly more complex decorator which converts the result of the decorated function to uppercase letters:
def uppercase(func):
... def wrapper():
... original_result = func()
... modified_result = original_result.upper()
... return modified_result
... return wrapper
Instead of simply returning the input function like the null decorator did, this uppercase decorator defines a new function on the fly(a closure) and uses it to wrap
The wrapper closure has access to the undecorated input function and it is free to execute additional code before and after calling the input function. (Technically, it doesn’t even need to call the input function at all.)
Note how, up until now, the decorated function has never been executed. Actually calling the input function at this point wouldn’t make any sense–you’ll want the decorator to be able to modify the behavior of its input function when it eventually gets called.
You might want to let that sink in for a minute or two. I know how complicated this stuff can seem, but we’ll get it sorted out together, I promise.
@uppercase
... def greet():
... return 'Hello!'
...
greet()
'HELLO!'
I hope this was the result you expected. Let’s take a closer look at what just happened here. Unlike null_decorator, our uppercase decorator returns a different function object
greet
<function uppercase.<locals>.wrapper at 0x7fb66510c0d0>
null_decorator(greet)
<function uppercase.<locals>.wrapper at 0x7fb66510c0d0>
uppercase(greet)
<function uppercase.<locals>.wrapper at 0x7fb6650e7f70>
And as you saw earlier, it needs to do that in order to modify the behavior of the decorated function when it finally gets called. The uppercase decorator is a function itself. And the only way to influence the “future behavior” of an input function it decorates is to replace(or wrap
That’s why uppercase defines and returns another function(the closure) that can then be called at a later time, run the original input function, and modify its result.
Decorators modify the behavior of a callable through a wrapper closure so you don’t have to permanently modify the original. The original callable isn’t permanently modified- its behavior changes only when decorated.
This lets you tack on reusable building blocks, like logging and other instrumentation, to existing functions and classes. It makes decorators such a powerful feature in Python that it’s frequently used in the standard library and in third-party packages.
A Quick Intermission
By the way, if you feel like you need a quick coffee break (of courses☕️)or a walk around the block at this point-that’s totally normal. In my opinion closures and decorators are some of the most difficult concepts to understand in Python.(yes I agree😄)
Please, take your time and don’t worry about figuring this out immediately. Playing through the code examples in an interpreter session one by one often helps make things sink in.
I know you can do it! Yes , we can do it! Just do it!!! ✌️
Applying Multiple Decorators to a Function
Perhaps not surprisingly, you can apply more than one decorator to a function. This accumulates their effects and it’s what makes decorators so helpful as reusable building blocks.
Here’s an example. The following two decorators wrap the output string of the decorated function in HTML tags. By looking at how the tags are nested, you can see which order Python uses to apply multiple decorators:
In [2]: def strong(func):
...: def wrapper():
...: return '<strong>' + func() + '</strong>'
...: return wrapper
...:
In [3]: def emphasis(func):
...: def wrapper():
...: return '<em>' + func() + '</em>'
...: return wrapper
Now let’s take these two decorators and apply them to our greet function at the same time.
You can use the regular @ syntax for that and just “stack” multiple decorators on top of a single function:
In [4]: @strong
...: @emphasis
...: def greet():
...: return 'Hello!'
What output do you expect to see if you run the decorated function?Will the @emphasis decorator add its tag first, or does @strong have precedence? Here’s what happens when you call the decorated function:
In [5]: greet()
Out[5]: '<strong><em>Hello!</em></strong>'
This clearly shows in what order the decorators were applied: from bottom to top. First, the input functions was wrapped by the @emphasis decorator. and then the resulting (decorated) function got wrapped again by the @ strong decorator.
To help me remember this bottom to top order, I like to call this behavior decorator stacking. You start building the stack at the bottom and then keep adding new blocks on top to work your way upwards.（nice👍)
If you break down the above example and avoid the @syntax to apply the decorators, the chain of decorator function calls looks like this:
decorated_greet = strong(emphasis(greet))
.
Again, you can see that the emphasis decorator is applied first and then the resulting wrapped function is wrapped again by the strong decorator.
This also means that deep levels of decorator stacking will eventually have an effect on performance because they keep adding nested function calls. In practice, this usually wont’ be a problem, but it’s something to keep in mind if you’re working on performance intensive code that frequently uses decoration.
Decorating Functions That Accept Arguments
All examples so far only decorated a simple nullary
If you try to apply one of these decorators to a function that takes arguments, it will not work correctly. How do you decorate a function that takes arbitrary arguments?
This is where Python’s args and *kwargsfeature for dealing with variable numbers of arguments comes in handy. The following proxy decorator takes advantage of that:
In [6]: def proxy(func):
...: def wrapper(*args, **kwargs):
...: return func(*args, **kwargs)
...: return wrapper
...:
These are two notable things going on with this decorator:
It uses the and ** operators in the wrapper closure definition to collect all positional and keyword arguments and stores them in variables(args and kwargs).
The wrapper closure then forwards the collected arguments to the original input function using the and ** “argument unpacking” operators.
It’s a bit unfortunate that the meaning of the star and double-star operators is overloaded and changes depending on the context they’re used in, but I hope you get the idea.
Let’s expand the technique laid out by the proxy decorator into a more useful practical example. Here’s a trace decorator (跟踪装饰器) that logs function arguments and results during execution time:
In [15]: def trace(func):
...: def wrapper(*args, **kwargs):
...: print(f'TRACE: calling {func.__name__}() '
...: f'with {args}, {kwargs}' )
...: original_result = func(*args, **kwargs)
...: print(f'TRACE: {func.__name__}() '
...: f'returned {orginal_result!r}')
...: return original_result
...: return wrapper
Decorating a function with trace and then calling it with print the arguments passed to the decorated function and its return value.
This is still somewhat of a “toy” example - but in a pinch it makes a great debugging aid:
@trace
def say(name, line):
return f'{name}: {line}'
say('Jane', 'Hello, World')
TRACE: calling say() with ('Jane', 'Hello, World'), {
}
TRACE: say() returned 'Jane: Hello, World'
'Jane: Hello, World'
Speaking of debugging, there are some things you should keep in mind when debugging decorators.
